HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

This is an unofficial implementation of paper HVNet. And the code is based on PCDet and PointCloudDynamicVoxel.

Please follow PCDet and PointCloudDynamicVoxel’s install guide.

remote:
	project on server for training
local:
	project on local machine to debug and I add some visualization code.

The author only provide bev result for Pose Loss, so I compare my result with paper.
Cyclist and Car don’t have too much different with paper, but Pedestrian is lower than paper for 4 point in AP

model: remote/output/pos_loss/checkpoint_epoch_66.pth
Pose loss result
Pedestrian [email protected], 0.50, 0.50:
bbox AP:78.9463, 74.2541, 70.1590
bev  AP:70.3723, 64.2458, 59.4957
3d   AP:64.3090, 57.9833, 52.6859
aos  AP:58.83, 55.68, 52.18
Cyclist [email protected], 0.50, 0.50:
bbox AP:92.2565, 77.7238, 74.9210
bev  AP:89.3720, 73.0727, 68.3603
3d   AP:84.5124, 67.7432, 63.2935
aos  AP:91.51, 76.41, 73.55
Car [email protected], 0.70, 0.70:
bbox AP:97.4905, 91.9816, 89.3797
bev  AP:94.4907, 88.2296, 85.4464
3d   AP:87.3334, 75.7501, 72.7637
aos  AP:97.42, 91.61, 88.81

For corner loss, I didn’t get a similar result with paper, but the training loss looks reasonable. Orange one is pos loss